Training Data Strategy for Machine Learning
If you want to eliminate inaccurate results and bias from your Machine Learning models, look no further than training, structuring, and updating your datasets. - Karthik Vasudevan, Founder Traindata Inc.,
Building AI and ML models to lead to a business solution is an evolutionary process.
Not all AI models lead to 100% accurate results. The accuracy of results evolves with more processing of relevant, high-quality data.
To put it simply, if you are trying to build an AI model to improve a product or fix loopholes at work, you need to ensure that your AI model learns every day from the data it processes.
This means that you need an effective data strategy to get the best out of your AI and ML models.
This post will guide you through four key factors that help you form a strong data strategy.
1 - Your data training budget
Estimating the budget of your AI/ML project will help you to define the following four things:
- The amount of time you want to invest in the project.
- The type of raw data you need for your model.
- The amount of training data you need.
- And how often you can afford or need to update your datasets.
2 - Your data source and data quality
The accuracy and success of your machine learning model are dependent on the source and quality of your data.
Suppose you are building a model to solve an external business need. In that case, you may choose to source your data from public domains, surveys, social media tools, synthetic data, acquired databases, and more.
If you build a model to solve an internal organizational need, you may source your data from departments and teams.
This is where your data engineers come into play. They do all the heavy lifting of sourcing the required data, convert and format the data for your AI/ML models.
Since the data your engineers obtain may be raw and unstructured, if you feed that data as is, your models won't make sense of it.
To make the data understandable to the AI/ML model, you must get the data annotated by experts. Domain experts.
If you are building an ML model to detect a disease from X-ray images, you need radiologists and medical professionals to annotate your image data.
If you are building an ML model that comprehends school test papers and marks grades automatically, you would need people for the education domain, preferably teachers, to annotate your data.
The cost of not getting the data annotation right is very high. The process of data annotation needs to be consistent and accurate throughout to prevent skewing of results.
To train a computer vision model for autonomous driving, you need to annotate tons of images and videos. Experts from the automotive, traffic and transport domains should annotate and define objects and elements from your data.
This is crucial to ensure they work perfectly fine when they are deployed in self-driving vehicles. And we haven't even started about the importance of eliminating biases in your training data.
3 - Data training partner
While it is easy to find employees from within your organization to help format and structure your data, you cannot ignore the impact of expert data training.
The previous point emphasizes the need to get your data trained, annotated, and prepared by experts to avoid inaccurate results that waste money and time.
You may crowdsource your data training and preparation tasks; it is an uphill task to hire and manage the entire thing.
You could get your data trained from the right set of people via a data training partner.
Data training is a big void that has given birth to many reputable data training vendors. These partners have experts from many different fields ready at hand and work with enterprise businesses to understand the data requirements and prepare data quickly and within budget.
At Traindata, we are a team of ex-Yahoo!s with over 15 years of experience in labeling, annotating, and training data for large AI/ML models. So we have the perfect set-up to train extensive data, get them annotated by real people from relevant domains and expertise. Visit our homepage to learn more.
4 - You need the right tech stack
As you define your budget to accommodate the timeline and the cost of sourcing and training data, you also need the right processes, tools, and procedures that complement your ambition to build an ML model.
When you require super-precise results and the need to feed massive volumes of data for processing, you need an equally powerful tech stack to streamline the process and deliver results.
That’s when you need faster machines, a better tech infrastructure, expert data annotators (or a team), and more to get closer to realizing your ambitions through your ML models.
Visit www.traindata.us to learn more about getting your data prepared and structured for your AI/ML models on time and budget.